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Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study.

ABSTRACT: BACKGROUND:Using existing data from clinical registries to support clinical trials and other prospective studies has the potential to improve research efficiency. However, little has been reported about staff experiences and lessons learned from implementation of this method in pediatric cardiology. OBJECTIVES:We describe the process of using existing registry data in the Pediatric Heart Network Residual Lesion Score Study, report stakeholders' perspectives, and provide recommendations to guide future studies using this methodology. METHODS:The Residual Lesion Score Study, a 17-site prospective, observational study, piloted the use of existing local surgical registry data (collected for submission to the Society of Thoracic Surgeons-Congenital Heart Surgery Database) to supplement manual data collection. A survey regarding processes and perceptions was administered to study site and data coordinating center staff. RESULTS:Survey response rate was 98% (54/55). Overall, 57% perceived that using registry data saved research staff time in the current study, and 74% perceived that it would save time in future studies; 55% noted significant upfront time in developing a methodology for extracting registry data. Survey recommendations included simplifying data extraction processes and tailoring to the needs of the study, understanding registry characteristics to maximise data quality and security, and involving all stakeholders in design and implementation processes. CONCLUSIONS:Use of existing registry data was perceived to save time and promote efficiency. Consideration must be given to the upfront investment of time and resources needed. Ongoing efforts focussed on automating and centralising data management may aid in further optimising this methodology for future studies.

SUBMITTER: Prospero CJ

PROVIDER: S-EPMC6715515 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study.

Prospero Carol J CJ Trachtenberg Felicia L FL Pemberton Victoria L VL Pasquali Sara K SK Anderson Brett R BR Ash Kathleen E KE Bainton Jessica J Dunbar-Masterson Carolyn C Graham Eric M EM Hamstra Michelle S MS Hollenbeck-Pringle Danielle D Jacobs Jeffrey P JP Jacobs Marshall L ML John Rija R Lambert Linda M LM Oster Matthew E ME Swan Elizabeth E Waldron Abigail A Nathan Meena M

Cardiology in the young 20190617 7

<h4>Background</h4>Using existing data from clinical registries to support clinical trials and other prospective studies has the potential to improve research efficiency. However, little has been reported about staff experiences and lessons learned from implementation of this method in pediatric cardiology.<h4>Objectives</h4>We describe the process of using existing registry data in the Pediatric Heart Network Residual Lesion Score Study, report stakeholders' perspectives, and provide recommenda ...[more]

PMID: 31204627

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Project description:One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.

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Dataset Information

Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study.

Publications

Lessons learned in the use of clinical registry data in a multi-centre prospective study: the Pediatric Heart Network Residual Lesion Score Study.

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